Nuclear magnetic resonance (NMR) spectroscopy is one of the most versatile tools available for probing biomolecular structure, dynamics, and interactions. It is the only method capable of determining molecular structure in solution at atomic resolution, and it has a host of important biomedical applications, including screening potential drug candidates and quantifying metabolites in biofluids or intact cells. Sensitivity and resolution present twin challenges in biomolecular NMR. Both are improved by performing experiments at the highest attainable magnetic field, on instruments that can be staggeringly expensive. Signal processing methods have also long been used in NMR to enhance sensitivity and resolution to the fullest possible extent. Linear methods of signal processing based on the Fourier transform invariably encounter a sensitivity/resolution tradeoff, where one can be enhanced at the expense of the other, but both cannot be simultaneously optimized. Nonlinear methods of spectrum analysis can in some cases avoid this tradeoff, and simultaneously improve both sensitivity and resolution. Anecdotal examples of simultaneously improving sensitivity and resolution in one dimension using maximum entropy deconvolution have been reported, but the gains achieved with this approach are typically modest. In this project we will develop methods for employing maximum entropy to simultaneously deconvolve two or more dimensions to enhance sensitivity and resolution. Preliminary data indicate the resulting gains in sensitivity and resolution are substantially increased compared to one-dimensional deconvolution, and are comparable to the gains associated with collecting data at much higher magnetic field.